CN111767939A

CN111767939A - Underwater sonar system target extraction method

Info

Publication number: CN111767939A
Application number: CN202010394202.4A
Authority: CN
Inventors: 陈耀武; 蒋荣欣; 刘雪松; 蒋施瑶
Original assignee: Zhejiang University ZJU
Current assignee: Zhejiang University ZJU
Priority date: 2020-05-11
Filing date: 2020-05-11
Publication date: 2020-10-13
Anticipated expiration: 2040-05-11
Also published as: CN111767939B

Abstract

The invention discloses an underwater sonar system target extraction method, which comprises the following steps: (1) converting the collected original sonar point cloud data into a color picture according to the echo intensity; (2) carrying out graying, binaryzation and expansion processing on the color picture in sequence to obtain a processed picture; (3) extracting connected regions of the processed picture, and screening and removing the connected regions with undersized areas and strength value pixels in the connected regions which do not meet requirements, wherein the rest regions are possible target regions; (4) extracting the characteristic vector of the possible target area after amplifying the possible target area; (5) carrying out target identification on the feature vectors of the possible regions by using a target identification model to obtain a target classification identification result; (6) and returning the color picture to draw specific target information according to the target classification and identification result. The method is simple in principle, strong in practicability and strong in adaptability to underwater acoustic signals.

Description

Underwater sonar system target extraction method

Technical Field

The invention belongs to the technical field of information processing, and particularly relates to an underwater sonar system target extraction method.

Background

Ocean and other underwater conditions have complex environments, and target extraction is difficult to perform through traditional optical signals, so that an underwater sonar system plays an important role. In the current technology of sonar systems, a method of observing objects in an underwater sonar system by human eyes is often adopted, so that observers need to receive a great deal of training and experience is accumulated. And the target extraction is greatly influenced by subjective factors, and cannot be deployed in the long-term and wide sonar target extraction application.

Patent application publication No. CN110837870A discloses a sonar image target recognition method based on active learning, and patent application publication No. CN107909082A discloses a sonar image target recognition method based on deep learning technology, and both methods are based on machine learning recognition, but the pretreatment is simple, and the recognition is inaccurate.

Disclosure of Invention

The invention provides an underwater sonar system target extraction method which is simple in principle, strong in practicability and strong in adaptability to underwater acoustic signals.

An underwater sonar system target extraction method comprises the following steps:

(1) converting the collected original sonar point cloud data into a color picture according to the echo intensity;

(2) carrying out graying, binaryzation and expansion processing on the color picture in sequence to obtain a processed picture;

(3) extracting connected regions of the processed picture, and screening and removing the connected regions with undersized areas and strength value pixels in the connected regions which do not meet requirements, wherein the rest regions are possible target regions;

(4) cutting color picture blocks corresponding to the possible target areas from the color picture in the step (1), amplifying the color picture blocks and then extracting feature vectors of the color picture blocks;

(5) carrying out target recognition on the feature vectors of the color picture blocks by using a target recognition model to obtain a target classification recognition result;

(6) and returning the color picture to draw specific target information according to the target classification and identification result.

Compared with the prior art, the invention has the beneficial effects that:

(1) the invention applies the machine learning technology to the underwater sonar system, has stronger technical innovation and can be continuously improved in the future.

(2) The invention has simple logic realization, strong pertinence and good practical effect by combining with actual signals and processing acoustic signals in the prior art.

(3) The invention can also carry out fine adjustment according to the actual application scene, and the relevant parameters can be corrected according to the actual situation so as to improve the accuracy of target identification.

(4) The target classification model can be adjusted based on actual classification requirements, and the whole target classification process is not influenced.

Drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.

FIG. 1 is a flowchart of an underwater sonar system target extraction method provided by an embodiment of the present invention;

FIG. 2 is a flow chart of a connected component screening process according to an embodiment of the present invention;

fig. 3 is a flowchart of a target extraction and classification algorithm provided in the embodiment of the present invention.

Detailed Description

In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be further described in detail with reference to the accompanying drawings and examples. It should be understood that the detailed description and specific examples, while indicating the scope of the invention, are intended for purposes of illustration only and are not intended to limit the scope of the invention.

Fig. 1 is a flowchart of an underwater sonar system target extraction method provided by an embodiment of the present invention. As shown in fig. 1, the underwater sonar system target extraction method provided by the embodiment includes the following steps:

(1) firstly, discrete point cloud signals collected by a sonar system are converted into color pictures colored according to the energy.

Specifically, the echo intensity value of the point cloud data is used as a pixel value, and the pixel values are arranged according to the corresponding position of the point cloud data to form a color picture. This step enables the direct conversion of a series of echo intensity data into a color picture that can be viewed directly by the human eye. The color picture can intuitively reflect the specific position of the target after the target extraction process is finished. The general experienced acoustic staff can make subjective target judgment directly according to the generated picture.

(2) And graying and binarizing the color picture.

By graying out the color picture, although a small portion of the precision is lost, the operation of a single channel is more convenient than the operation of RGB three channels. The data length of each pixel point can be better reduced by carrying out binarization operation on the black and white picture, so that the subsequent steps are more convenient.

(3) And performing expansion processing on the binarized picture.

Since the return data of the underwater sonar signals are discrete point data, the omission of some points in the middle is likely to occur, and the data points which are not adjacent are likely to be the same target. The best expansion times of the step in different water areas are different, and during expansion treatment, the expansion treatment is generally carried out for 1-7 times, and fine adjustment can be carried out according to actual conditions. Experiments prove that the expansion times are preferably 4-5, the same target area is not communicated if the expansion times are too few, and noise and other non-target communication if the expansion times are too many.

(4) And acquiring the connected domain from the expanded picture.

After the step (3), most targets are communicated, namely connected with each other, and a continuous whole is represented on the picture. Specifically, contour detection is performed on each binary suspected area in the processed picture, and all connected areas are obtained. This step enables a set of connected regions to be obtained including the target.

(5) And screening the connected areas to obtain possible target areas.

Compared with noise points and backgrounds, data points of underwater targets have the characteristics of large size and high strength value, as shown in fig. 2, the process of screening connected regions to obtain possible target regions is as follows:

firstly, judging whether the area of a connected region is large enough or not aiming at the connected region to be screened, and if not, considering the connected region as background noise and continuously screening other connected regions;

then, judging whether the intensity value of the data point in the connected region meets the requirement or not according to the connected region with a large enough area, storing the connected region meeting the requirement into a connected region result set as a possible target region, regarding the connected region not meeting the requirement as a non-final target, and continuously screening other connected regions.

In the embodiment, whether the area of the connected region is large enough is determined by comparing the area of the connected region with an area threshold, and when the area of the connected region is larger than the area threshold, the area of the connected region is considered to be large enough, and the area threshold is set according to the size of the recognition target, and is not limited herein.

In the embodiment, whether the intensity value of the data point in the connected region meets the requirement is judged by counting whether the number of the points with the intensity values higher than the intensity preset value exceeds a number threshold, the connected region with the data of the points exceeding the data threshold is regarded as meeting the requirement, and the connected region is taken as a possible target region and stored in a connected region result set. The intensity preset values and the number are all related to the recognition target, and are set according to the related attributes (size, material, etc.) of the recognition target, and are not limited herein.

Through the step, whether the target exists underwater or not and the size and the position of the target under the condition of the target can be basically obtained. The result set after this step can basically determine whether or not there is an object, but cannot determine information such as the type of the object.

(6) Amplification and feature extraction of connected regions.

Cutting color picture blocks at the same position from the color picture obtained in the step (1) by the possible target area obtained in the step (5), and amplifying the cut color picture blocks to a specific size for feature extraction. Specifically, an SIFT feature extraction algorithm is adopted to extract feature vectors of the amplified color image blocks, and the cut image is amplified integrally so as to facilitate extraction of more feature points. The target characteristic vector can be well extracted from the background through the step.

(7) Dimension reduction of the feature vectors and object classification.

In this embodiment, as shown in fig. 3, a target recognition model is used to perform target recognition on the feature vectors of the color picture blocks, so as to obtain a target classification recognition result. Specifically, the target recognition model comprises a BOW model and an SVM model which are determined by parameters;

performing dimensionality reduction on the feature vector by using a BOW model determined by the parameters;

and carrying out target recognition on the dimensionality reduction vector by using the SVM model determined by the parameters to obtain a target classification recognition result.

The BOW model and the SVM model need to be obtained through massive data training in advance. After the step, which type of the preset target the target belongs to can be completely judged, and information such as the size, the direction and the like of the target can be known.

(8) And returning the color picture to draw specific target information according to the target classification and identification result.

In the embodiment, the direction, size and category information of the target can be specifically drawn by returning the color picture according to the target classification and identification result.

According to the underwater sonar system target extraction method, the point cloud data are converted into the color pictures, the target is skillfully identified according to the point cloud data, and then the target is identified by machine learning, so that the accuracy and efficiency of target identification are improved. The method can be well applied to an actual underwater sonar system, can greatly save cost, and improves the objective accuracy of target extraction.

The above-mentioned embodiments are intended to illustrate the technical solutions and advantages of the present invention, and it should be understood that the above-mentioned embodiments are only the most preferred embodiments of the present invention, and are not intended to limit the present invention, and any modifications, additions, equivalents, etc. made within the scope of the principles of the present invention should be included in the scope of the present invention.

Claims

1. An underwater sonar system target extraction method is characterized by comprising the following steps:

(4) cutting color picture blocks corresponding to the possible target areas from the color picture in the step (1), amplifying the color picture blocks and extracting feature vectors of the color picture blocks;

2. The underwater sonar system target extraction method according to claim 1, wherein in step (1), the echo intensity values of the point cloud data are used as pixel values, and the pixel values are arranged according to positions corresponding to the point cloud data to form a color picture.

3. The underwater sonar system target extraction method according to claim 1, wherein in the step (2), the expansion process is performed 1 to 7 times during the expansion process.

4. The underwater sonar system target extraction method according to claim 1, wherein in step (3), contour detection is performed on each binarized suspected area in the processed image to obtain all connected areas.

5. The underwater sonar system target extraction method according to claim 1, wherein in the step (3), the process of screening possible target areas is as follows:

firstly, screening a connected region with the area larger than an area threshold value;

then, for the connected regions larger than the area threshold, counting whether the number of points with the intensity values higher than the intensity preset value exceeds a number threshold, wherein the connected regions exceeding the number threshold are possible target regions.

6. The underwater sonar system target extraction method according to claim 1, wherein in step (5), a SIFT feature extraction algorithm is used to extract feature vectors of the amplified color picture blocks.

7. The underwater sonar system target extraction method according to claim 1, wherein the target recognition model includes a parametric deterministic BOW model and an SVM model;

8. The underwater sonar system target extraction method according to claim 1, wherein in step (6), color pictures are returned according to target classification recognition results to depict the direction, size and category information of the targets.